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Implementing intelligent, touchless order management

Abhishek Bikram-Singh
3 Aug 2022

Introducing smart technology, fixing master data issues, and applying business rule-driven supply chain operations can help organizations pre-empt the root causes of manual interventions to drive agile, frictionless, and intelligent order management.

Touchless and agile order-to-delivery is one of the six pillars that underpin the intelligent supply chain. Everyone remotely linked to supply chain management understands how important this can be. Every “touch” you remove adds more accuracy and speed to the process.

  • The six pillars of the independent supply chain:
    1. Intelligent network design and risk management
    2. Smart forecasting and integrated business planning
    3. 360° sourcing analysis and supplier collaboration
    4. Touchless and agile order to delivery
    5. Supply chain as a service
    6. Supply chain control tower and end-to-end performance management

In fact, in the last couple of years, the word “touchless” has become the most used adjective to describe a supply chain. The only other word that comes close to competing with it is that other word in our pillar descriptor – “agile.”

The agility of a supply chain is complicated: it depends on multiple factors cutting across internal and external functions, which everybody understands. By contrast, though, isn’t touchlessness supposed to be a simple goal to achieve? That’s why I’d like to focus here on order management, where we see a lot of manual work and interventions.

In recent years, I’ve worked with client supply chain teams across multiple industries to make their order management touchless. In our first meetings, the general consensus was to implement electronic data interchange (EDI) and to develop and deploy robotic process automation (RPA) platforms as a shortcut solution to achieve “touchless” order management.

I wish the answer were this simple – but even after making these changes, the desired result for our clients was not achieved. And no, it wasn’t because the implementation was poor, either.

Where does touch happen…?

To gain a better understanding, we need to go a little deeper into this topic. At what stages typically does “touch” happen in order management?

The first stage is order entry. Orders are created manually: they are received from customers in an unstructured email, or in purchase order copies, or by phone or fax (yes, fax is still not obsolete). Any errors or corrections in these sales orders as notified by EDI or by RPA routines are also fixed manually – adjustments to replenishment orders, for example.

The next stage is stock allocation. Quantities are confirmed manually in the system before outbound deliveries are handed over to the warehouse for dispatch, and allocated stock is adjusted based on channel and customer priorities.

The third stage of manual intervention is outbound delivery. Adjustments sometimes need to be made in confirmed quantities due to stock constraints, wrong batch selections, and limited availability of credit, for example.

… and why?

But what are the root causes of “touches” – even though tools, systems, and automation are already in place?

  • Missing automated processes and/or systems – especially those that capture customer orders and stock allocation. As a result, purchase order copies are processed manually by an operator to make a sales order in the system. There is often also a lack of integration between CRM and ERP systems, as a result of which orders are manually created. The list can be endless
  • Master data issues – not having the right supply chain masters is one of the biggest culprits, which can cancel out attempts to achieve full touchless order management. Let’s say an organization has established EDI connections or implemented RPA for order entry, but the product mapping is not adequately maintained, resulting in exceptions rather than a sales order. Customers may send orders using an old SKU code which no longer exists, or which causes a price mismatch. If master data boundaries such as minimum order quantity (MOQ) and lot size are not set, what ought to be automated processes are liable to require manual intervention
  • Ad hoc adjustments – when stock levels are limited, how do organizations allocate inventory to sales orders? If the available credit limit does not cover the entire sales order value, which SKUs can be trimmed? All these scenarios need managing with ad hoc adjustments rather than with clear rule sets.

Solving the problem – intelligent order management

We’ve seen that missing systems, master data management (MDM) issues, and a lack of business rules are among the root causes of manual interventions in order management. What can be done? Here are some steps to consider.

  • Introduce tools and platforms to automate manual steps
    • Implement replenishment orders for your distributors based on stock norms. Give them a platform for confirmation with a defined tolerance
    • Implement EDI with customers wherever possible for direct integration for sales order entry in your system
    • Implement an automated stock allocation system.
  • Fix MDM
    • Fix master data, remembering it’s not a one-time exercise but a continuous adjustment throughout the product life cycle.
    • Communicate changes in your product, promotions, price list, etc., to customers to avoid issues in automated sales order creation.
  • Business-rule-driven operations
    • Define the sales order validation rules, and implement and automate them
    • Review and revise credit control policies regularly, rather than once or twice a year. A growing business frequently renders existing credit limits inadequate, and can block delivery
    • Define business rules for stock allocation. Find what suits your business needs: is it proportionate, or a fair share allocation if stock levels are constrained? Define prioritization based on channels and customers to avoid manual adjustments every time.

Order management is a living process. It requires constant monitoring to pre-empt and fix potential causes of touch.

At Capgemini, we assist organizations in their pursuit of “touchless” order management, helping them find root causes in the value chain, and introducing intelligent technology to fill the gaps.

Delivering rule based, automated order fulfilment process and quick turnaround for one of the world’s largest CPG companies

This consumer packaged goods company and global supply chain leader needed was running decentralized order management operations and warehouse-based order processing that was creating a number of challenges, including:

  • A lack of standardized processes
  • Significant manual intervention and processing
  • A lack of information on non-allocation of stocks
  • No capture of fill rates due to missing pristine orders
  • No information flows on slow moving and non-moving stock

Capgemini deployed its propriety order management system to automate the process from order entry to out bound delivery, centralized the client’s entire operations, and created standard operating procedures to drive rule-based order validation and stock allocation.

This led to a range of business outcomes, including the elimination of incorrect order generation, priority allocation of stock over channels and for segmented customers within channels, increased warehouse efficiency, increased transparency on order vs. allocation and non-allocation, and a highly responsive and robust query handling process.

In turn, this unlocked enhanced business outcomes:

  • 10% increase in order fill rate
  • 7% increase in service level
  • Increased speed to market
  • Material availability raised from 50% to 90%.

About author

Abhishek Bikram-Singh

Managing Consultant,Intelligent Supply Chain Operations,Capgemini’s Business Services
Abhishek has over 15 years of industry experience in managing different supply chain functions. He has worked with clients across industries to define their current order fulfilment and MDM maturity with respect to people, process, technology, and governance, and to develop their target operating model.

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